Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415744 | Computational Statistics & Data Analysis | 2006 | 26 Pages |
Abstract
Consider the three-component time series model that decomposes observed data (Y) into the sum of seasonal (S), trend (T), and irregular (I) portions. Assuming that S and T are nonstationary and that I is stationary, it is demonstrated that widely used Wiener–Kolmogorov signal extraction estimates of S and T can be obtained through an iteration scheme applied to optimal estimates derived from reduced two-component models for S plus I and T plus I. This “bootstrapping” signal extraction methodology is reminiscent of the iterated nonparametric approach of the US Census Bureau's X-11 program. The analysis of the iteration scheme provides insight into the algebraic relationship between full model and reduced model signal extraction estimates.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Tucker McElroy, Andrew Sutcliffe,